Why Chatbots Are Not As Popular As You Might Think
If you check on the latest tech trends, chatbots have been a lot in the news as the next big thing. There has been a lot of hype about how successful they would become. Not to mention the speculation of how they would take the world by storm. But this yet to happen! In today’s post, we will look at why chatbots did not live to become as popular as we expected.
But first things first.
What is a chatbot?
A bot is a computer program. It helps automate routine tasks by chatting with a user via a conversational interface. Chatbots are powered by artificial intelligence. They can therefore understand complex requests and offer personalized responses and enhance interactions over time.
The sets of rules that govern most chatbots are set by humans through a Bot building platform. Developers play a crucial role in determining how each of the conversations is scripted, and the kind of experience users should expect when interacting with bots.
Why do they fail?
Chatbots sometimes fail to deliver user experiences that are as efficient, seamless and delightful as wished-for. Here are some of the reasons why chatbots fail or may not be as popular as you may think:
· Artificial intelligence is not that accessible
Most chatbots are not actually intelligent. Since they are created based on a decision tree logic, they base their responses on specific keywords identified in the user’s input. This simply means that the intelligence of these types of bots depends on the capacity, thoughtfulness, and patience of the programmer or designer who developed it to predict all potential user use cases and inputs.
Even when a developer takes time to think of every possible scenario, life might still fail to fit into those boxes. To add to that, bots with natural language learning and linguistic capabilities are still rare.
· Use cases are not that strong
Whenever a new technology is put out in the world, developers and designers get really excited about it. Chatbots are no exception. What we saw when bots were first introduced was a gold rush of companies doing their best to be the first in their category to successfully deploy a bot. The result is an excess of bots solving irrelevant problems or offering poor experiences.
We have to learn a lot from our failures before we can deploy smart and relevant bots. Before developing a bot, it would be useful to answer the following questions:
Does this product really need a bot?
Do I have the patience to build a bot that will do exactly what I want it to do?
Are there platforms that can support its functionality?
Most bot developers tend to bypass such questions. No wonder most these bots never become revolutionary.
· Lack of transparency
Successful chatbots are transparent. Right from the beginning, they let the user know that they are chatting with a robot, not a human being. Knowing that they are chatting with a machine makes the users more forgiving about some of the mistakes that the bots may make.
While we all want to use bots that feel as human as possible, you don’t want to deceive your users. Pretending like your bot is human will set unrealistic expectations in the user’s mind and eventually lead to loss of trust when these expectations are not met. Try chatting with our bot and let us know if you found it efficient.
· Bots don’t understand context
Aren’t humans good at conversations? They read between the lines, understand sarcasm, and constantly leverage contextual information when they give you a response.
Bots do not have this capacity. Unless in cases where they are powered by natural language processing technology, they can only hold contextual information for a few chat bubbles. They end up losing track of what the user said before they posed the question.
· They don’t communicate with existing business systems
When creating chatbots, designers are often tempted to rebuild functionality from scratch. Bots should be created as part of a larger ecosystem. Isolating it from the other business systems can be harmful to customers as well as your business.
For example, let’s take the case of a bot built for booking appointments in an office. The chatbot should have a way of communicating with the offices existing appointment management system. Otherwise, the office manager will have extra work trying to handle requests coming through the new channel. It will also mean lack of consistency for the user.
· Chatbots try to handle too many things at once
It looks like designers are so excited about the numerous tasks a bot can help execute that they forget to narrow down its area of focus.
For instance, reports indicate that 70% of the bots on Facebook messenger fail at fulfilling their simple user requests. This is usually as a result of designers failing to narrow their bot down to one area of focus.
Designers should keep in mind that it would be better to have bots doing one thing well that to have bots doing multiple things poorly.
· Chatbots lack proper human escalation protocols
Users like to know that they can still rely on humans whenever technology fails. However, most chatbots do not have an escalation workflow to allow a human to take over the conversation when the bot is unable to help. They therefore end up leaving users hanging and sometimes, more frustrated than they were when they started the conversation.
· People still prefer to talk to other people
As we highlighted earlier, conversations are made up of way more than text. Bots are not able to keep up with such things as sarcasm and context. Even bots that are fired by NLP can only go as far as producing processed content, but they still cannot compare to humans. No matter how much wit and human-like mannerisms are incorporated into a bot, it will still be no match for a human.
All said and done. Innovators were not entirely wrong. We can continue to use bots to help us with repetitive, low level and automated queries and tasks. They can still function as cogs in much larger and complex systems.
However, chatbots are nowhere close to the expectations created by the 21st century hype. Computers are simply not good at understanding human emotion. They are still unable to understand how we feel or even what we are asking them.
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